Marker combination and application thereof in diagnosis of expression metaplasia of spasmolysis polypeptide

Abstract

The invention discloses a marker combination and application thereof in diagnosis of expression metaplasia of spasmolysis polypeptide. The marker combination comprises an SCD1 marker combination, a PHB1 marker combination and a p-ERK marker combination. The marker combination is based on joint detection of SCD1, PHB1 and p-ERK markers, the sensitivity and the specificity of an established diagnosis model are remarkably superior to those of a traditional single detection method, and early recognition of the potential precancerous lesion SPEM is facilitated.

Description

Technical Field

[0001] This invention belongs to the field of biomarkers, specifically relating to a combination of biomarkers and their application in the diagnosis of antispasmodic peptide expression. Background Technology

[0002] Currently, gastric metaplasia, especially spasmolytic polypeptide-expressing metaplasia (SPEM), is considered a potential precancerous lesion, and its diagnosis primarily relies on pathological examination and immunohistochemical techniques. The main diagnostic method is multiplex immunofluorescence assay, which uses various specific antibodies to label cell subsets in the gastric mucosa, such as TFF2, MUC6, and GIF, for co-expression analysis to identify SPEM cell populations. Although some progress has been made in SPEM detection, the following limitations remain: Limited biomarker selection: Single biomarkers often struggle to balance sensitivity and specificity in complex pathological contexts. While multi-label combined detection has improved this, selecting the most diagnostically significant combination from numerous potential biomarkers remains a challenge. Complex and costly detection methods: Existing immunohistochemical detection techniques and multiplex staining methods are cumbersome to operate, and repeatability and accuracy are difficult to guarantee. Furthermore, the high cost restricts the widespread application of this technology in primary hospitals and large-scale screening. Summary of the Invention

[0003] This invention aims to address the following key technical challenges: How to construct a high-precision SPEM diagnostic model based on multiple biomarkers, by systematically screening and combining multiple molecular biomarkers closely related to SPEM to achieve diagnostic standards with both high sensitivity and specificity. How to optimize detection methods to reduce operational complexity and cost: By improving the detection process and introducing semi-quantitative analysis methods, the model becomes simpler, more efficient, and adaptable to large-scale clinical screening needs while maintaining accuracy. How to enhance the model's personalized risk assessment and clinical applicability: Utilizing statistical methods and machine learning algorithms, a diagnostic tool capable of providing individualized risk assessment is constructed, enabling physicians to accurately formulate intervention measures based on patients' specific indicators. How to provide guidance for subsequent treatment through this diagnostic model: The diagnostic model is not only used for early detection of SPEM but also for monitoring lesion progression, thereby guiding clinical targeted intervention strategies to achieve early prevention and reversal of the disease process.

[0004] To address the aforementioned technical problems, this invention provides a combination of biomarkers and their application in diagnosing spasmolytic peptide expression chemopreservation. Through multi-biomarker combined detection and comprehensive statistical modeling, this invention achieves the following significant technical effects: Improved diagnostic accuracy and early identification rate: The diagnostic model established based on the combined detection of SCD1, PHB1, and p-ERK biomarkers is significantly superior to traditional single-detection methods in terms of sensitivity and specificity, facilitating early identification of SPEM, a potential precancerous lesion; Simplified detection process and reduced costs: The use of semi-quantitative immunohistochemistry and standardized data processing procedures makes operation simpler, reduces detection costs, and possesses high repeatability and scalability; Personalized risk assessment: By constructing a nomogram tool, an intuitive assessment of individualized patient risk is achieved, providing strong evidence for clinicians to formulate precise intervention plans; Guiding clinical intervention and pathological reversal: This invention is not limited to diagnosis but also provides data support for subsequent interventions, indicating a comprehensive management model from early detection to pathological reversal, which is expected to reduce the risk of gastric cancer.

[0005] The first aspect of the present invention provides a combination of markers, including a combination of SCD1, PHB1 and p-ERK markers.

[0006] SCD1 (Stearoyl-CoADesaturase 1) is an enzyme located in the endoplasmic reticulum that catalyzes the synthesis of unsaturated fatty acids, an important component of cell membrane lipids. It plays a crucial role in cell proliferation, survival, inflammatory responses, and cancer development.

[0007] PHB1 (Prohibitin 1) is a highly conserved protein, primarily located in the inner mitochondrial membrane, and is an important regulator of mitochondrial function. PHB1 plays a role in various cellular processes, including cell cycle regulation, cellular senescence, apoptosis, and the stability of the mitochondrial respiratory chain.

[0008] p-ERK (Phosphorylated Extracellular Signal-Regulated Kinase) is the phosphorylated form of ERK (extracellular signal-regulated kinase) and a key molecule in the MAPK (mitogen-activated protein kinase) signaling pathway. The ERK signaling pathway is widely involved in the regulation of cell proliferation, differentiation, growth, and apoptosis.

[0009] A second aspect of the present invention provides the use of the combination of biomarkers as described in the first aspect of the present invention in the preparation of medicaments for predicting and / or diagnosing the expression of antispasmodic peptides.

[0010] In a preferred embodiment, the expression levels of the biomarker combination differ significantly between the antispasmodic peptide-expressing phantom samples and the non-antispasmodic peptide-expressing phantom samples.

[0011] A third aspect of the present invention provides the application of a reagent for determining a combination of biomarkers in the preparation of a kit for determining the expression of antispasmodic peptides in a subject, wherein the combination of biomarkers includes SCD1, PHB1, and p-ERK.

[0012] In a preferred embodiment, the sample is a mucosal sample, such as a mucosal sample from the greater curvature of the stomach.

[0013] In a preferred embodiment, the reagent is a reagent for determining the level of a marker.

[0014] A fourth aspect of the present invention provides a reagent for detecting a combination of biomarkers as defined in the first aspect of the present invention, wherein the reagent is a reagent for determining the level of the biomarkers, for example, a reagent for immunohistochemical staining or Western blot.

[0015] Preferably, the marker is derived from a mucosal sample, such as a mucosal sample from the greater curvature of the stomach.

[0016] The fifth aspect of the present invention provides a diagnostic kit for the expression of antispasmodic peptides, comprising the reagents and controls as described in the fourth aspect of the present invention.

[0017] A sixth aspect of the present invention provides a diagnostic system for the expression of antispasmodic peptides, the diagnostic system comprising a detection module and an analysis and judgment module; the detection module detects the level of a combination of biomarkers in the mucosa of a subject and transmits the level data to the analysis and judgment module; the analysis and judgment module judges the level data of the biomarker combination and outputs a diagnostic result: the subject is either an expression of antispasmodic peptides or an expression of non-antispasmodic peptides; wherein the biomarker combination is as defined in the first aspect of the present invention.

[0018] In a preferred embodiment, the analysis and judgment module determines the level data of the biomarker combination by: determining whether there is a significant difference in the expression level of the biomarker combination in the antispasmodic peptide-expressing phantom sample and the non-antispasmodic peptide-expressing phantom sample.

[0019] The present invention also provides a computer-readable storage medium storing a computer program, characterized in that, when the computer program is executed by a processor, it can perform the functions of the diagnostic system as described in the sixth aspect of the present invention.

[0020] The present invention also provides an electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor is configured to execute the computer program to implement the functions of the diagnostic system as described in the sixth aspect of the present invention.

[0021] Based on a large sample of cases, this patent utilizes a semi-quantitative immunohistochemical detection method to simultaneously detect key indicators such as SCD1, PHB1, and p-ERK in gastric mucosal specimens. Spearman correlation analysis and LASSO regression are then employed to screen for the most sensitive and specific biomarker combinations. Multiple statistical and machine learning algorithms, including logistic regression (LR) and XGBoost, are used to comprehensively analyze the data, establish and optimize the diagnostic model, and verify the model's diagnostic efficacy by calculating the AUC value using ROC curves. A nomogram incorporating multiple variables is also constructed to achieve personalized risk assessment. This provides clinicians with a one-stop solution from early diagnosis to intervention and treatment.

[0022] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0023] Unless otherwise specified, all reagents and raw materials used in this invention are commercially available.

[0024] The positive and progressive effects of this invention are as follows: This invention achieves the following significant technical effects through multi-marker joint detection and comprehensive statistical modeling methods:

[0025] Improving diagnostic accuracy and early identification rate: The diagnostic model established based on the combined detection of SCD1, PHB1 and p-ERK biomarkers is significantly superior to traditional single detection methods in terms of sensitivity and specificity, which helps to identify SPEM, a potential precancerous lesion, at an early stage.

[0026] Simplified testing process and reduced costs: The use of semi-quantitative immunohistochemistry technology and standardized data processing procedures makes the operation simpler, reduces testing costs, and has high repeatability and scalability.

[0027] Achieving personalized risk assessment: By constructing a nomogram tool, we can achieve an intuitive assessment of individualized risks for patients, providing a strong basis for clinicians to develop precise intervention plans;

[0028] Guiding clinical intervention and pathological reversal: This invention is not limited to diagnosis, but also provides data support for subsequent interventions, indicating a comprehensive management model from early detection to pathological reversal, which is expected to reduce the risk of gastric cancer. Attached Figure Description

[0029] Figure 1In the diagram: A is a schematic diagram of immunohistochemistry and semi-quantitative analysis of gastric tissue microarray in obese patients; B is a schematic diagram of the correlation analysis between SPEM occurrence and SCD1, Trim21, PHB1, and p-ERK; C and D are schematic diagrams of LASSO analysis (C: green represents PHB1, black represents SCD1, blue represents Trim21, and red represents p-ERK); E shows the construction of the regression model and XGBoost for the diagnostic model; F is the nomogram results.

[0030] Figure 2 In the diagram, (A) the SHAP summary plot shows the impact of each variable on the probability of SPEM diagnosis; (B) the SHAP waterfall plot for a single patient shows the contribution of features to the prediction of SPEM risk; and (C, D) the confusion matrices show the performance of the logistic regression (C) and XGBOOST (D) models in SPEM classification on the training set. Detailed Implementation

[0031] The present invention is further illustrated below by way of embodiments, but the invention is not limited to the scope of the embodiments described herein. Experimental methods in the following embodiments that do not specify specific conditions were performed according to conventional methods and conditions, or as selected according to the product instructions.

[0032] Example 1: Sample Collection and Immunohistochemical Detection

[0033] 1.1 Sample Source and Ethical Approval

[0034] Sample selection: Forty-two severely obese patients who underwent laparoscopic sleeve gastrectomy (LSG) at the Obesity Center of Shanghai Tenth People's Hospital were selected, and preoperative gastroscopy showed no malignant lesions.

[0035] Tissue harvesting: During the operation, a tissue of approximately 1×1×1cm was harvested from the greater curvature of the gastric body. 3 Mucosal biopsy specimens were immediately fixed in 4% paraformaldehyde solution for 24 hours.

[0036] Ethical Approval: This study was approved by the Ethics Committee of Shanghai Tenth People's Hospital, and all patients signed informed consent forms.

[0037] The following table shows the inclusion and exclusion criteria for obese cases:

[0038]

[0039]

[0040] 1.2 Immunohistochemical (IHC) detection

[0041] Paraffin embedding and sectioning: After dehydration, clearing, and paraffin embedding of the fixed tissue, it was sectioned on a microtome with a thickness of 4μm.

[0042] Antigen retrieval: The sections were microwaved in citrate buffer (pH 6.0) for 10 minutes for retrieval.

[0043] Blocking: Treatment with 3% hydrogen peroxide for 10 minutes to inactivate endogenous peroxidase, followed by blocking with 5% goat serum for 30 minutes.

[0044] Primary antibody incubation: Anti-SCD1 (Abcam, ab19862, 1:200), anti-PHB1 (Proteintech, 10774-1-AP, 1:150), and anti-p-ERK (Cell Signaling Technology, #4370, 1:100) were added and incubated overnight at 4°C.

[0045] Secondary antibody and staining: Incubate HRP-labeled secondary antibody (Dako, K4001) at room temperature for 30 minutes, develop with DAB for 2 minutes, and counterstain with hematoxylin.

[0046] Mounting and scoring: After dehydration and clearing, slides were mounted with neutral resin. Two pathologists assessed staining intensity and the percentage of positive cells in a blinded manner.

[0047] The results are as follows Figure 1 As shown in A, based on the number and staining intensity of immunohistochemically positive cells, they are classified into +, ++, and +++, respectively, for semi-quantitative grading. Figure 1 B) in the table is used for subsequent statistical analysis.

[0048] Example 2: Data Statistical Analysis and Model Building

[0049] 2.1 Data Preprocessing and Grouping

[0050] Expression data: Import the raw scores of the biochemistry scores (1-3: low, medium, high) into R (v4.2.1).

[0051] SPEM definition: Based on immunofluorescence multicolor staining of pathological sections, the groups were defined as SPEM group (n=18) and non-SPEM group (n=24).

[0052] 2.2 Correlation Analysis

[0053] Use the Spearman rank correlation test to output the r-value and p-value.

[0054] Result: As Figure 1 As shown in B, SPEM levels were significantly negatively correlated with SCD1 (r = -0.379, P = 0.013) and TRIM21 (r = -0.376, P = 0.014), and significantly positively correlated with PHB1 (r = 0.435, P = 0.001) and p-ERK (r = 0.480, P = 0.001).

[0055] 2.3 LASSO Feature Selection

[0056] Tools: R package glmnet (v4.1-4), 10-fold cross-validation, the optimal λ is determined by the minimum mean square error method.

[0057] Filtering results: such as Figure 1 As shown in C and D, the final remaining variables are SCD1, PHB1, and p-ERK.

[0058] 2.4 Model Construction and Performance Evaluation

[0059] Logistic Regression: Using the R package rms, a multivariate logistic regression model was constructed, and the results are as follows. Figure 1 As shown in E, AUC = 0.942.

[0060] XGBoost: Python version of xgboost (v1.6.1), results are as follows Figure 1 As shown in E, AUC = 0.89.

[0061] 2.5. Construction of Personalized Risk Estimation Tools - Development of Noctilinear Chart Model

[0062] Software environment: R packages rmda (v1.6) and rms (v6.2-0).

[0063] Input variables: SCD1, PHB1, p-ERK semi-quantitative score.

[0064] Output: SPEM occurrence status.

[0065] Column chart ( Figure 1 The F) annotation in the figure shows the score range for each variable and the mapping relationship between the total score and the risk probability.

[0066] Example 3: Feature Importance Interpretation—SHAP Analysis

[0067] Tools: Use the Python package 'shap' (v0.41.0) to perform interpretive analysis on the trained model.

[0068] SHAP summary diagram: as shown Figure 2 As shown in A, p-ERK, PHB1, and SCD1 are the three variables that contribute the most to SPEM prediction, among which high expression of p-ERK has the most significant positive effect.

[0069] SHAP waterfall diagram: as shown Figure 2 As shown in B, the composition of the predictive value of an individual SPEM patient is illustrated, with high expression of p-ERK and PHB1 being the main risk-enhancing factors.

[0070] Example 4: Model Performance Validation—Confusion Matrix Evaluation

[0071] Logistic Regression (LR) Model: Built using the R package 'rms', with performance as follows Figure 2 As shown in C. The model achieved an accuracy of 0.833 (95% CI: 0.6864–0.9303) on the training set, a Kappa value of 0.6711, a sensitivity of 0.9444 and a specificity of 0.7500, and an F1 score of 0.8293, indicating that the model has good discriminative ability against SPEM.

[0072] XGBOOST model: Trained using the Python version of 'xgboost' (v1.6.1), performance is as follows. Figure 2 As shown in D in the figure. The model accuracy was 0.875 (95% CI: 0.7101–0.9649), the Kappa value was 0.7241, the sensitivity and specificity were 0.9000 and 0.8636, respectively, and the F1 value was 0.8182, showing better overall performance and generalization ability than logistic regression.

Claims

1. A combination of markers comprising SCD1, PHB1 and p-ERK markers.

2. The use of the biomarker combination as described in claim 1 in the preparation of a drug for predicting and / or diagnosing the expression of antispasmodic peptides.

3. The application as described in claim 2, characterized in that, The expression levels of the biomarker combination differed significantly between pharmacochemical samples expressing antispasmodic peptides and those expressing non-antispasmodic peptides.

4. The application of reagents for determining biomarker combinations in the preparation of kits for assessing the expression of antispasmodic peptides in subjects, wherein, The combination of markers includes SCD1, PHB1, and p-ERK.

5. The application as described in claim 4, characterized in that, The sample is a mucosal sample, such as a mucosal sample from the greater curvature of the stomach.

6. The application as described in claim 5, characterized in that, The reagent is used to determine the level of the marker.

7. A reagent for detecting a combination of biomarkers, characterized in that, The biomarker combination is as defined in claim 1, and the reagent is a reagent for determining the level of the biomarker, such as a reagent for immunohistochemical staining or Western blot. Preferably, the marker is derived from a mucosal sample, such as a mucosal sample from the greater curvature of the stomach.

8. A diagnostic kit for the expression of antispasmodic peptides, comprising the reagent and control as described in claim 7.

9. A diagnostic system for the expression of antispasmodic polypeptides, characterized in that, The diagnostic system includes a detection module and an analysis and judgment module; the detection module detects the level of a combination of biomarkers in the subject's mucosa and transmits the level data to the analysis and judgment module; the analysis and judgment module judges the level data of the biomarker combination and outputs a diagnostic result: the subject is a phantom of antispasmodic peptide expression or a phantom of non-antispasmodic peptide expression; wherein, the biomarker combination is as defined in claim 1.

10. The diagnostic system as described in claim 9, characterized in that, The analysis and judgment module determines the level data of the biomarker combination by: determining whether there is a significant difference in the expression level of the biomarker combination in the antispasmodic peptide expression phantom sample and the non-antispasmodic peptide expression phantom sample.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it can perform the functions of the diagnostic system as described in claim 9 or 10.

12. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that, The processor is used to execute the computer program to implement the functions of the diagnostic system as described in claim 9 or 10.

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